Impact/Purpose:

This study demonstrates that similarities and variability in new particle formation (NPF) characteristics can be partly explained by satellite-based measurements of atmospheric composition. Proxy models of total ultrafine particle (UFP) concentrations that expand the suite of remote sensing predictors exhibit improved variance explanation relative to simpler models that have been previously proposed. These empirical models may be diagnostically used to explain some of the observed spatial variability in characteristics of NPF and reported variations in the closure between models based on simplified nucleation schemes and observations, leading to improved schemes suitable for global atmospheric models. Given the likely impact of NPF on climate, and substantial uncertainty and model-to-model variability in simulating NPF, improved treatment of NPF in global models is critical for improved understanding of aerosol-climate impacts.

Description:

New particle formation (NPF) can potentially alter regional climate by increasing aerosol particle (hereafter particle) number concentrations and ultimately cloud condensation nuclei. The large scales on which NPF is manifest indicate potential to use satellite-based (inherently spatially averaged) measurements of atmospheric conditions to diagnose the occurrence of NPF and NPF characteristics. We demonstrate the potential for using satellite-measurements of insolation (UV), trace gas concentrations (sulfur dioxide (SO2), nitrogen dioxide (NO2), ammonia (NH3), formaldehyde (HCHO), ozone (O3)), aerosol optical properties (aerosol optical depth (AOD), Ångström exponent (AE)), and a proxy of biogenic volatile organic compound emissions (leaf area index (LAI), temperature (T)) as predictors for NPF characteristics: formation rates, growth rates, survival probabilities, and ultrafine particle (UFP) concentrations at five locations across North America. NPF at all sites is most frequent in spring, exhibits a one-day autocorrelation, and is associated with low condensational sink (AOD×AE) and HCHO concentrations, and high UV. However, there are important site-to-site variations in NPF frequency and characteristics, and in which of the predictor variables (particularly gas concentrations) significantly contribute to the explanatory power of regression models built to predict those characteristics. This finding may provide a partial explanation for the reported spatial variability in skill of simple generalized nucleation schemes in reproducing observed NPF. In contrast to more simple proxies developed in prior studies (e.g. based on AOD, AE, SO2, UV), use of additional predictors (NO2, NH3, HCHO, LAI, T, O3) increases the explained temporal variance of UFP concentrations at all sites.